---
name: longbridge-ml-strategy
description: |
  Machine-learning prediction strategy framework via Longbridge Securities — walk-forward rolling training with feature engineering (MACD, RSI, Bollinger Band width, volume change rate) and a scikit-learn classifier (Random Forest / Gradient Boosting); retrains every 60 days, predicts 5-day direction probability; evaluates win rate, profit factor, and Sharpe ratio. For reference only — not investment advice. Triggers: "机器学习", "ML策略", "预测模型", "随机森林", "梯度提升", "深度学习", "AI选股", "walk-forward", "機器學習", "ML策略", "預測模型", "隨機森林", "梯度提升", "machine learning", "ML strategy", "predictive model", "random forest", "gradient boosting", "AI stock selection", "walk-forward", "rolling training", "feature engineering", "scikit-learn", "XGBoost".
license: MIT
metadata:
  author: longbridge
  version: "1.0.0"
  risk_level: read_only
  requires_login: false
  default_install: true
  requires_mcp: false
  tier: analysis
---

# longbridge-ml-strategy

Walk-forward machine-learning framework for stock direction prediction. Fetches historical OHLCV data, engineers technical features, trains a rolling classifier (Random Forest or Gradient Boosting), generates probabilistic buy/sell signals, and evaluates backtest performance.

> **Response language**: match the user's input language — Simplified Chinese / Traditional Chinese / English.

## Dependencies

Requires: `scikit-learn`, `pandas`, `numpy` (usually pre-installed).
Optional: `xgboost` or `lightgbm` for gradient-boosting models.
If unavailable, fall back to a simpler logistic-regression model.

## When to use

- User asks for ML-based prediction, rolling model training, feature-importance analysis, or AI-driven entry/exit signals for a single stock.
- Triggers: "用机器学习预测 TSLA 涨跌", "NVDA random forest strategy", "walk-forward backtest AAPL".

## Workflow

1. Fetch 504 daily candles (≈ 2 years):
   `longbridge kline <SYMBOL> --period day --count 504 --format json`

2. **Feature engineering** (compute on rolling windows):
   - MACD line and signal (EMA12 − EMA26, signal EMA9)
   - RSI-14
   - Bollinger Band width: (upper − lower) / mid, window 20
   - Volume change rate: (vol_t − vol_{t-5}) / vol_{t-5}
   - 5-day price momentum: (close_t / close_{t-5}) − 1
   - Label: 1 if close_{t+5} > close_t × 1.01, 0 if < close_t × 0.99, else drop

3. **Walk-forward training**:
   - Training window: 252 days; retrain every 60 days
   - Model: `RandomForestClassifier(n_estimators=100)` or `GradientBoostingClassifier`
   - Predict probability for the current bar

4. **Signal generation**:
   - prob > 0.60 → 模型上涨概率偏高 / Model upside probability elevated
   - prob < 0.40 → 模型下跌概率偏高 / Model downside probability elevated
   - Otherwise → 模型无方向性预测 / No directional signal from model

5. **Backtest metrics** (on out-of-sample predictions):
   - Win rate (% correct directional calls)
   - Profit factor (gross profit / gross loss)
   - Annualised Sharpe ratio (assuming daily rebalance)
   - Max drawdown

6. **Feature importance**: rank top-5 features by mean decrease in impurity.

Run `longbridge kline --help` to confirm flag names before calling.

## CLI

```bash
longbridge kline --help

longbridge kline <SYMBOL> --period day --count 504 --format json
```

## Output

| Metric | 简体 | 繁體 | English |
|---|---|---|---|
| Current signal | 当前信号 | 當前訊號 | Current signal |
| Signal probability | 预测概率 | 預測概率 | Signal probability |
| Win rate | 胜率 | 勝率 | Win rate |
| Profit factor | 盈亏比 | 盈虧比 | Profit factor |
| Sharpe ratio | 夏普比率 | 夏普比率 | Sharpe ratio |
| Max drawdown | 最大回撤 | 最大回撤 | Max drawdown |
| Top features | 重要特征 | 重要特徵 | Top features |

Output: current signal box → backtest summary table → feature importance list → caveats (past performance, data snooping). Cite **Longbridge Securities** / **数据来源：长桥证券** / **數據來源：長橋證券**.

> 以上内容仅供参考，不构成投资建议。投资决策请结合自身风险承受能力独立判断。
> The above is for reference only and does not constitute investment advice. Investment decisions should be made based on your own risk tolerance.

## Error handling

| Situation | 简体回复 | 繁體回復 | English reply |
|---|---|---|---|
| `command not found: longbridge` | 回退到 MCP 或提示安装 longbridge-terminal | 回退到 MCP 或提示安裝 longbridge-terminal | Fall back to MCP or install longbridge-terminal |
| `not logged in` / `unauthorized` | 请运行 `longbridge auth login` | 請執行 `longbridge auth login` | Run `longbridge auth login` |
| `scikit-learn` not found | 提示 `pip install scikit-learn pandas numpy`，并改用逻辑回归降级 | 提示安裝，降級至邏輯回歸 | Prompt install; degrade to logistic regression |
| Fewer than 252 candles | 数据不足，无法完成 walk-forward 训练 | 數據不足 | Insufficient data for walk-forward |
| Other stderr | 直接显示原始错误 | 直接顯示原始錯誤 | Surface verbatim |

## MCP fallback

When the CLI is unavailable, fall back to the MCP server. Discover available tools from the MCP server's tool list at runtime.

## Related skills

- `longbridge-kline` — OHLCV data source
- `longbridge-volatility-strategy` — vol regime as an additional feature
- `longbridge-multifactor` — cross-sectional factor signals complement

## File layout

```
longbridge-ml-strategy/
└── SKILL.md
```